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19.02.2020 bis 20.02.2020

Miguel Hernán / Katalin Gémes: "Causal inference - learning what works"

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Veranstaltung ausschließlich für Fachpublikum

Intensive Short Course, Feb 19 - 20, 2020

Flyer Causal Inference, February 19 - 20, 2020

The course introduces students to a general framework for the assessment of comparative effectiveness and safety, with an emphasis of the use of routinely collected data in healthcare settings. The framework relies on the specification and emulation of a hypothetical randomized trial: the target trial. The course explores key challenges for causal inference and critically reviews methods proposed to overcome those challenges. The methods are presented in the context of several case studies for cancer, cardiovascular, and renal diseases.


Course objectives: To learn how to determine “what works” using data from observational and randomized studies 

After successful completion of this course, students will be able to:

  • Formulate sufficiently well-defined causal questions for comparative effectiveness research
  • Specify the protocol of the target trial
  • Design analyses of observational data that emulate the protocol of the target trial
  • Identify key assumptions for a correct emulation of the target trial

Pre-course reading: Chapters 1-3 of the book Hernán MA, Robins JM (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC, forthcoming. The book can be downloaded (for free) from

1.25 ECTS


  • 312,50 €
  • 212,50 € for enrolled students (proof required)



Miguel Hernán (Departments of Epidemiology and Biostatistics, Harvard T.H. Chan School of Public Health)
Katalin Gémes (Karolinska Institute, Sweden)

Miguel Hernán conducts research to learn what works to improve human health. Together with his collaborators, he designs analyses of healthcare databases, epidemiologic studies, and randomized trials. Miguel teaches clinical data science at the Harvard Medical School, clinical epidemiology at the Harvard-MIT Division of Health Sciences and Technology, and causal inference methodology at the Harvard T.H. Chan School of Public Health, where he is the Kolokotrones Professor of Biostatistics and Epidemiology. His edX course Causal Diagrams and his book Causal Inference, co-authored with James Robins, are freely available online and widely used for the training of researchers. Miguel is an elected Fellow of the American Association for the Advancement of Science and of the American Statistical Association, an Editor of Epidemiology, and past Associate Editor of Biometrics, American Journal of Epidemiology, and the Journal of the American Statistical Association.


Berlin School of Public Health / Institute of Public Health


09:00 - 17:00 Uhr


Charité Campus Mitte


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